Nic Neate (00:00.078)
or just saying that's not enabled.
Ben Pearce (00:03.64)
You've got headphones on so it doesn't matter. That's for folks that have got the, not got headphones on and then that's useful. So if you are ready, I shall get going.
Nic Neate (00:05.486)
Okay, great.
Ben Pearce (00:21.664)
this down. Okay here we go.
Ben Pearce (00:30.734)
Hey folks and welcome to the Tech World Human Skills Podcast. Well, this is a very special episode today. Why? Well, this episode is a recording of a fireside chat from the cloud and AI infrastructure stage at Tech Show London. Well, sort of. The tech set up was a bit hard to do it live and we can make this slightly longer on the podcast. So.
So it's another version of the conversation that happened at Tech Show London on the stage. Now, we're talking about building out an AI native engineering team. Software engineering is definitely one of the earliest industries to be massively changed by generative AI. So rather than talk about how AI has helped around the edges of a team,
we're talking about a team that's fundamentally changed. Now, our guest today has led an engineering department for the last year and a half on that very journey. He's got some amazing lived experience and insight to share with us. So please welcome to the show, CTO of Nimbus, Nick, neat Nick, it is brilliant to have you with us.
Nic Neate (01:51.288)
Ben, it's an honour. Thanks for having me. It's great to see you again. And yeah, can't wait to explore this because as you say, it's a really, really important and really relevant conversation right now.
Ben Pearce (02:03.152)
It sure is and and literally as we've been prepping for for both the tech show london recording Which is actually happening next week. We're recording this but as we've been prepping for that and prepping for this Article out after article after article has been hit in our news feeds talking about this very topic and we've been like picking each other on whatsapp Have you seen this article? Have you seen this article? So it feels like one of those topics that is just the top
of everybody's mind at the moment, top of everybody's newsfeed. And so it's great to have a conversation about it. But before we get into the detail, just for all the lovely listeners that have never heard of you before, Nick, or met you before, could you tell us a little bit about your background and what you're doing?
Nic Neate (02:47.718)
Where have you been? So yeah, I'm Nick and I start by saying I'm probably one of the first people who's actually kind of lost their job to AI as a software engineer. I was at Microsoft a couple of years ago and my whole department got sacked off, the reason being that Microsoft wanted to invest in AI instead and therefore we were no longer the strategic priority. So I've kind of lived that experience of
and kind of lost that income and that purpose to AI. But then going into this job with Nimbus at CTO, where I'm now leading a product team, leading an engineering team, and riding that wave and just going on that transformation and in a slightly different way, because we're not getting rid of our team. We're sort of in the opposite of what we're doing in our approach to AI native and how to do that.
that well. So that's what I want to talk about.
Ben Pearce (03:50.766)
Well, you I'm really excited to have this proper sit down, this proper conversation. I think the first thing I'd like to talk about is to maybe define what an AI native engineering team. mean, everybody is AI everything at the moment. And often that's vaporware. You know, we're just tagging the word AI on what we've always done before. But I don't think that's the case with you. So could you tell me what you mean by an AI native engineering team?
Nic Neate (04:20.76)
Yeah, I think that's really important because I want to be real about it and not just kind of sugarcoating everything and telling you that you can do anything you want with AI. So let's start with what is vibe coding because that's kind of also kind of a key buzzword and something which is transforming software teams. so by vibe coding, I mean using AI to write code where it's an assistant to a human.
But it goes beyond that. It's a humanist is there driving it. They're prompting it. They're sat there with cursor, with VS code, whatever it is. And the AI is doing all of the actual coding work and splitting things out. Yeah, I mean, have you got an experience of live coding, Ben? Is that something that you've tried yet?
Ben Pearce (05:06.159)
Do you know what? Not really, not in a vibe coding. So I have prompted and got code created for me that I've then read and gone, yep, that does what I want. And I've then deployed that. But I would not say that was before the term vibe coding has come along. So I'm gonna say, I'm gonna say I don't really know it. So no.
Nic Neate (05:27.054)
So I think to kind of understand what's happening, I would strongly recommend that anyone who's got any kind of engineering background, even if they haven't done it for ages or even if they're much too senior to actually look at the code anymore, have a go. To switch, it's dead easy just to, if you've got an IDE, enable the AI part of it, copilot, recursor, whatever it is, and just see what happens because it's an incredible transformative experience.
you do that. you think about the first time you used ChatDBT years ago back in the day and you're like, hold on, this is like a person who knows stuff and has great ideas, is talking to me, multiply that by 100 for the experience of, I've just created this web app from scratch and I didn't even know how to do it myself and it's there and it works. So that's that's live coding and it's like having divine powers. It's like you speak into your computer and out of the
swirling chaos of zeros and ones in the memory, these incredible creations just spring into being. So that's kind of a starting point for AI native. And maybe six months ago, I'd have been saying, you know, that's AI native. But the thing about AI native is obviously it's a journey, it's a spectrum. And the tooling, the capabilities keep on changing and getting better. So there's no single goalpost that you can say this is AI native, we've now done it. We're getting on a journey from
Originally, humans did it all. They wrote every line of code by hand. Then there was AR assistants who started giving suggestions of what the next line might be. And then they started writing the whole thing. And that's, you know, in the Bat-Vibes coding phase. Then it's kind of moved on to supervised, where you're, maybe you've got an agentic coding system, you're telling it, this is the requirement, and it just goes away.
and works on that in the background and then comes back when it's finished and says, here's your PR, review that, if you're happy, then we're good to go. And then maybe beyond that, it gets onto the AI working completely independently. But that sounds dangerous. So we need some cautions over where we actually, how far on this journey we want to go, what guardrails we need in place for safety and responsible use of AI within it as well.
Ben Pearce (07:47.263)
So when you're saying AI native, what you're saying is your main production code, the stuff that you do, that your team do for a living is created with vibe coding and AI tools.
Nic Neate (08:01.794)
And by coding, agentic coding, it's the things right now, it's AI native means you're riding the wave of this AI tooling, which is constantly coming through and supercharging your delivery velocity, but also dealing with the reality of that, because it's not like it just happens and everything's suddenly rosy. So there's all kinds of issues you work through. So you're maximizing the agency of the AI tools. You're bringing in
Ben Pearce (08:16.174)
Yeah.
Ben Pearce (08:24.889)
Yeah.
Nic Neate (08:29.41)
the new systems, the new capabilities as they appear, but also you're retaining that human control, that human accountability as to what gets delivered and actually makes it into the code base.
Ben Pearce (08:43.823)
Okay, so that's a good overview of what we're talking about. So my next question therefore, and you've already started to talk about it, but why would you wanna do this? There's a lot of shiny stuff out there, AI slop you could talk about as well. There's lots of shiny stuff with AI, but why would you want to create an AI native engineering team?
Nic Neate (09:06.584)
So to me, that's almost not even a question because that's like saying, I've got this incredible logistics business and it's a super efficient and highly professional, well-organized team who do it all with horse and cart and do that perfectly while we don't want to use a lorry. so there's a carrot side of it, which is whatever your business is, I assume your business is doing something awesome in the world.
Ben Pearce (09:09.759)
Okay
Ben Pearce (09:24.963)
Yeah. Yeah.
Nic Neate (09:36.492)
and you would love for it to do more of it. And being AI native enables you to do so much more of it. And there's a stick as well, which is I assume your business also has competitors and you want to continue to exist as a business. And riding this wave and keeping up with the AI native aspect of your business is crucial to doing that. So in a nutshell, that's why.
Ben Pearce (09:59.791)
And so if we were to break that apart from the, say, we're in the business, so I know we've all got businesses, but we're in the business of creating software, say, for that business, whatever it might be. So is AI native gonna make your team more productive? Is it gonna raise the speed of what you're doing? Is it gonna raise the quality of what you, what's the things that it's gonna do better for you as the business of a software engineering team?
Nic Neate (10:10.926)
Mm.
Nic Neate (10:26.198)
It's all of those things. Everything that is a measured output of your team in terms of delivery to the business or to the customer, what it's doing, can be made better through being an AI native. But you don't just flick a switch and it is better. It's a transformation. It's a whole different skill set and way of working that you move into. And it's navigating that journey, which is the challenge for engineering leaders and engineering teams in today's world.
Ben Pearce (10:28.857)
Yeah.
Ben Pearce (10:56.015)
Okay, so basically, so far we've said this is a rosy future of everything that you do and create is gonna be better in an AI native landscape, why would you not do it? That's kind of what I'm hearing from you so far.
Nic Neate (11:12.812)
And yes, and better in terms of the what is possible. But it's you know, you're going to be facing challenges within that. And you might be an individual who's not so happy about the parts of your job, which you don't get to do anymore. Or you might be facing particular sort of nightmares with context switching, and trying to keep up with different agents doing different things all the time. So it doesn't mean that you're
Ben Pearce (11:39.777)
Yeah. Yeah.
Nic Neate (11:43.042)
your mental health is better necessarily. So understanding that and navigating that is part of, we want to make this better for everyone, better for the individuals and better for the business.
Ben Pearce (11:49.87)
Yeah.
Ben Pearce (11:53.327)
So guess what you're saying is the output of your function, the output of your team is better as a result of AI but that doesn't mean that it's a silver bullet, that it's not difficult, that there's not transitions, that there's not things to the... But ultimately you're saying that the output is going to be a lot better of the team as a whole if you're embracing some of these new technologies. So what sort of things have you embraced and what sort of things have you done at Nimbus?
Nic Neate (12:14.093)
Absolutely.
Nic Neate (12:21.166)
So we've tried a lot of things. It's been a hugely exciting time to be anywhere in tech and to be at a company which is sort of small scale up, has got the agility and the flexibility just to jump in and have a go at something, see if it works and move on. But also in a company that's in a competitive situation and has a necessity to innovate and to move forwards and to try and get ahead.
I could give you some examples. We've tried getting product managers to write code, create product using vibe coding, using lovable. Is that a quicker way to get something viable to market? And that's not something we've been able to make work out for us when actually the rubber hits the road and you try and take that purely vibe coded thing and run it in production as part of your tech stack.
we've tried getting an enthusiastic data engineer to create a software product and quickly code something which is incredible demo, but all kinds of issues with the code base again, that when you want to do proper DevOps with it or you want to maintain it and enhance it, that it suddenly becomes very expensive. trying these things is a key part of the journey and all of the fun of the journey.
and some of it works, some of it not so much.
Ben Pearce (13:52.279)
Right? So, but you've been experimenting, trying things and trying to figure out what's working really well. And it's interesting there because like when you started saying, hey, this is better, you've got divine powers, I think it's like you've got divine powers that you can just type in and actually you're saying, well, we tried it with a data engineer and actually it didn't work quite right. And we tried it with some product managers and it didn't quite, so it's not quite at.
divine powers, think it and it is now perfect in front of you. There's still lots of things that need to happen to make good quality production ready software using.
Nic Neate (14:29.954)
Yeah, so that kind of comes on to the kind of the skill set shift and the skills that you need in order to deliver engineering, deliver software, deliver your function to the highest quality and to the highest velocity, making the best use of these tools. And what does it mean to have a highly efficient, high performing engineering team in this new world? we all...
We all say, we all believe that we've got this high performing engineering team. We all tell aboard that's what we've got, certainly. Have you? Because the people that make up that team, the processes and the way that they work are very different now to what they were 12 months ago. So unless you've been evolving your team and kind of keeping pace and transforming, then I'm not convinced that you still do. And that's why it's important to be trying things and to be on this journey.
Ben Pearce (15:24.047)
Well, shall we jump into that? Should we unpack that? Because I think that's really interesting. So, you know, if you're thinking now about a software engineering team today versus one a year ago, two years ago, three years ago, what are those skills of an AI native software engineer?
Nic Neate (15:43.786)
Yeah, because it is going back to that kind of that example of you've got a transportation logistics company that's very skilled with with horses, then the skills, the people that you need to drive lorries and coordinate lorries and be mechanics on lorries is going to be very different. So we need to understand that shift and what the skill set is. So a couple of examples with with engineering and developers.
One of the things that we look for that we valued most highly a couple of years ago was the deep focus, the ability to get locked in, to zone in on a task, to achieve that flow state and to be fully motivated and at your most productive and just churn out some reams of code, doing something new and incredible within a few days or a few weeks. And that suddenly is
completely not the job anymore. Because however good you were at it, and however much fun you had doing that, AI can do it better. It can do it in 10 minutes, what you could do in a week. That's exactly the thing that AI is brilliant at, is when it's got the context, when it knows what it's doing, is just creating good quality code. And the skill set has now shifted to how do I make the AI do that?
Ben Pearce (16:44.398)
you
Nic Neate (17:11.436)
rather than how do I do it myself? So that's a big shift for someone who was incredibly prized for their just coding output, their deep focus ability a couple of years ago. Code review, similar. The people who could really do a detailed low level code review and find the salient, the important problems at that early stage and save tons of time down the line in testing and fixing problems after they hit the field.
Again, that's no longer the key skill anymore because that, again, is something AI is very good at. You don't have to review every line of code in the way that you used to. And the way you do code review is changing.
Ben Pearce (17:56.937)
So I guess, so some examples of what it's not. So it's no longer deep focus coding. It's no longer reading code, checking code, code reviews, that kind of stuff. So those skills that you may have had before are not what are gonna make you really thrive in this new world. So what are the sorts of skills that are gonna make you thrive in this new world?
Nic Neate (18:00.332)
Yeah.
Nic Neate (18:17.934)
So number one for me is context engineering. Because the context is what turns AI from a novelty into something which is super powerful and amazing. As a CTO, as an engineering leader, we've all suddenly got the ability to spin up an engineering team which rivals Microsoft's in terms of the number and the quality of the brains which can create product.
Ben Pearce (18:21.711)
Okay?
Nic Neate (18:47.694)
create code. Why aren't we all doing that? It's because we don't have the ability to do the context engineering, which gives the AI the salient relevant information that it needs to write the right code to move your product, to move your function, move your business forwards. So the key skill as an engineer is in driving AI by giving it the right context. And that can be
prompting at a simple level, how do you communicate most clearly in a few sentences to the AI, the simple task that you want it to do. So that communication and that's kind of clear English language communication is suddenly a very important skill for engineers where, know, if you're the kind of engineer who's actually better at communicating in C sharp than you are in English, then you're going to struggle to make that transition and to be a strong engineer.
in an AI native world. But it's not just the prompting, it's the broader context as well. So how do you bring the documentation, the understanding of your whole stack, your architecture, the bigger picture of what you're building into the narrow task that an AI is doing?
Ben Pearce (20:02.031)
So skill number one was context engineering. So that is being able to effectively describe in the prompt, this is what I am trying to achieve. Is that sort of outlining the vision of where your product is heading or is that, can you unpack that a bit more for me?
Nic Neate (20:25.464)
So there's the big picture context. It needs to understand your company and what your business does so that it knows the domain it's working in. There's the product context. This is my tech stack. This is the wider architecture. You might have 15 code bases that make up a broader product, and your AI is working on one of them as part of a feature. But it needs to understand the
the bigger picture in order to make the right decisions within that one code base. So that's how do you encapsulate that knowledge and bring it in? And then there's the specific task and the requirements around what that's doing. it's kind of, there's multiple levels of context. Some of those are going to be context, which is sort of intrinsic to the company, the business doesn't change very much. Some of it is very specific to the task.
and getting that structure in so that you can easily give any given agent the right context at the right time. That's context engineering. And that's what makes AI something which really supercharges your output rather than just something which autocompletes some code for you.
Ben Pearce (21:41.815)
And so when you, you you're talking about some of your failed experiments where you'd maybe say taken a data engineer or a product manager and gotten to try and vibe code and create something, was that part of the challenge that they perhaps didn't understand that full context to be able to prompt it in the right way to create something that was ultimately quite useful?
Nic Neate (22:02.764)
Yeah, and those were earlier stage things as well. that, you know, the context windows and the MCP server tools that are available to bring in Coqtext have developed since then as well. So I don't want to be down on the individuals involved in those experiments because they were successful in some ways as well. They just, you know, they turned out not, you know, embracing failure is a key part of this. But yeah, it's the
Ben Pearce (22:19.767)
Yeah. Yeah.
Ben Pearce (22:27.223)
Yeah. Yeah.
Nic Neate (22:32.47)
You do those things, you work out why they don't work. You understand, what's the new context and what's the changing process we need to bring in to make it work. And that's kind of the bigger picture of how context engineering works and how you change your workflow, change your tool set in order to get better efficiency, better output from the agents and enable them to do what they do best, which is that core coding bit.
Ben Pearce (22:58.873)
Okay, so context engineering, that was thing one, the skills that new software engineers need. Any others?
Nic Neate (23:09.966)
Absolutely. I say decision making, that's another key one. this is it kind of relates to context switching as well. Because part of this kind of empowering AI is also putting the right guardrails around AI and getting making decisions at the right level and making sure the human is pulled into be the one who's the accountable and
says yes or no to those decisions. And it used to live in this world where as a manager, as a tech lead, you could define a task, you could give it to a junior engineer, and you could let them run with it for a couple of days before you kind of you check in. It depends on the individual, And then you come back and you repriorities or adjust their direction and set them going again. And you had time to line up the next thing, you had time to do
some more kind of strategic big picture thinking before you had to kind of go back to that individual. With AI, it comes back to you in five minutes, 10 minutes. And so you've got the ability to set lots of agents off. If you're experienced in vibe coding, you'll probably find you often got multiple VS code windows up and working on one product here, working another one over here and maybe another one over there. And you set the agent going and it does some stuff and then you come back to it.
And you're constantly having to come back and get back into the headspace for that task and make a decision, do some communication, get the right action in place, and then set it off again. So that efficiency of understanding a situation and making a decision and moving on, that's a key skill, which you used to be able to do at a much more relaxed cadence. But if you're going to maximize your
Ben Pearce (24:46.403)
Hmm.
Nic Neate (25:08.064)
AI native output, then you need to be much, much faster. So getting better at zoning in, making decision, moving on. That's key skill that you can develop, but is a skill you need to adopt to be successful in this space.
Ben Pearce (25:23.023)
And also, you know, you're talking a lot, there's a lot of context switching there, isn't there? So, you know, whereas we started at the beginning talking about deep focus, it's not deep focus anymore. It's about, and maybe, you know, you're not creating the individual lines of code. You're not really into the real weeds of everything, but you need to understand the high level to then be able to make those decisions, switch context, think about, so you're constantly at those difficult high intensity points.
rather than in the churn out a bit of easy stuff to, you know, that allows me to get some headspace.
Nic Neate (25:56.33)
Exactly. Exactly. Yeah. And that kind of leads on to number three, which is being the one who's actually being the leader, showing the initiative, bringing the ideas and driving the direction. So it's that kind of higher level leadership role and bringing that to as many AI agents as you're able to control and make decisions for. The AI is not the thing that's bringing the initiative to
your product, your output, that remains for now a key human skill. So being the person who can do that, bring in the ideas, identify the things which are blocking that you're slowing down the AI, any kind of bureaucracy that is getting in the way and clearing that and being that, you know, talking to other teams, getting consensus, putting another tool in place that allows the AI to do something which it couldn't do before.
those are the skills that you're bringing to overall kind of tune the machine, improve the efficiency of the function that you're doing, rather than doing those things individually yourself all the time.
Ben Pearce (27:09.743)
Okay. So we've had three things so far. Remind me of the three things we've had. Context engineering, we've had decision making and the third thing was.
Nic Neate (27:19.854)
bringing the ideas and the initiative.
Ben Pearce (27:22.093)
Right, and the others.
Nic Neate (27:24.854)
And I think those are the most important ones that you want to focus on. We could go through more, but I think that's plenty to get stuck in with.
Ben Pearce (27:36.847)
That's the thing. And so that's a very different job to what it was a year ago. Two years ago, yeah.
Nic Neate (27:42.518)
Absolutely. It's more of a senior role. It's more of a lead role. It's bringing all kinds of experience that you've got as someone who's worked on different projects, got battle scars, knows the things which are going to make a system work and aren't going to work, and combining that with delegation, communication, bringing teams together, and unblocking and making the project happen.
Ben Pearce (28:14.271)
So we thought about what it is, we thought about why it's good, we thought about the skills that you do need. Maybe let's think about some of the challenges and some of the objections. The first one that springs to my mind is that's a completely different set of skills. And so the people that you've got,
doing that might not want to be the people that are doing this new thing. And the analogy that sort of comes to my mind is back to the Industrial Revolution and like weavers, weavers were there weaving clothes. And then with the Industrial Revolution, suddenly now we need machine operators and people that wanted to weave were not then people that wanted to operate machines. It feels like they're almost very different jobs.
Nic Neate (29:04.054)
Indeed. that's potentially a leadership challenge. You're going to have some people who are hugely enthusiastic, who are early adopters, who are kind of leading the way in a new way of working, who find it naturally fits a skill set in themselves, which is coming to the fore. And that's fantastic. And you're onto a big winner, and they can become the advocates and the thought leaders within your team.
you probably are going to have other people who struggle and who don't want to adopt a change to something they were very comfortable with, or who maybe they want to, but it's not their natural skill set. And you you need to work on them in terms of your training, your education, try and bring them on the journey. But also you need to think about your organization and what is the skill set we need in this team and probably bring in
some other people who are more aligned with the new normal, the new optimal for the roles that you've got.
Ben Pearce (30:07.426)
Yeah. Yeah. So, yeah, could be a massive, well, it is a massive skills change. Now, the other thing that sort of come to my mind is just that fatigue of context switching. So, you know, I've never been a professional developer, but I do remember earlier in my career where I used to debug a lot of code when I was in a support team and I used to have Windows source code on my desk and we'd be trying to debug. you know, I'd have a queue of, let's say, 20 cases.
and I'd be spending half an hour on this one and I go right to the weeds and then I have to context switch into a completely different technology to a completely different customer, a completely different support case. That context switching was hard and then I do half an hour of that and I'll be on to the next one. That felt like when I had a real day like that, like my brain was gonna dribble out of my ear. The mental fatigue of all of that context switching and trying to handle all of that in my brain.
Nic Neate (30:57.143)
Yeah.
Ben Pearce (31:02.64)
Are you seeing that kind of thing from people that are in this sort AI native world?
Nic Neate (31:08.13)
I can certainly relate to that. I can say we see it within our team as well. And it's, again, I'd say that's a skill. It's a muscle that you can develop. It's not something which the brain fundamentally can't do. It's something which the brain often finds difficult. But the more you practice it, the better it can get. A bit like solving quadratic equations or
or whatever it is. So that's, that's something not to say means this doesn't work. It's something which you need to kind of focus on and develop individually if you're someone who's in that kind of role. And, and probably the more that you use AI and the better you are with it, the more you'll find that's an issue. So this is is kind of, it's kind of the Jevons paradox.
Have you kind of looked at the Jevons paradox? it's kind of an analogy that's been used quite a lot in the AI world over the recent months. So Jevons was like an economist in the 19th century or something, and he was looking at steam trains. And he noticed this weird phenomenon that as the steam engines got more and more efficient, the total demand for coal went up and up and up. So each individual
Ben Pearce (32:12.248)
No, end the lights in me.
Nic Neate (32:34.978)
journey with a steam train, use less coal, and yet more and more coal was needed in the economy. And the reason, of course, was that total number of journeys increased because that efficiency made people start to see all kinds of use cases for railways and things, you know, I can now grow lettuce in California and have them in a market in New York when they're out of season in New York because that efficiency has come.
So suddenly the railway business goes into a massive boom. If you're an engineer who is good with AI, then you are the steam train in this analogy. You've suddenly become hugely more efficient in what you can do. You're able to produce much more output because you're using AI to do it. Therefore, the demand on you has gone through the roof and you're incredibly busy and stressed.
in a way that you weren't before when you didn't have this wonderful tool that could do your job for you. So it's hugely kind of frustrating and counterintuitive to be living that reality. But that's something that we need to get the balance on. And we need to get the value for those individuals and get the reward, but also enable them to manage their time and their context switching and to do this in a sustainable and achievable way.
Ben Pearce (33:37.552)
Mm.
Ben Pearce (34:02.456)
Yeah, yeah, no, that's interesting. I've not come across that before. Now, the other thing or another thing, earlier on, you were talking about the people orchestrating these AI agents. They're of a more senior persuasion. So what I mean by that is they understand the context. They understand the context of the business, the context of the architecture, those sorts of things. So what about
the juniors, where do the juniors go or where do the seniors come from? Because you can't suddenly become a senior unless you've got that experience of being the junior. So where's the pipeline or where do all those junior type roles go or what happens there?
Nic Neate (34:47.746)
Yeah, and so there was an article from Microsoft recently about their approach to kind of early in career developers and how they see that evolving. Did you see that one and the kind of...
Ben Pearce (35:03.554)
I did. This is the one I think that was written by Mark Razinovich and Scott Hanselman I saw published it. And the reason I know because Scott has been on this podcast before. So, yes, I did see that they had sort of created an academic paper on their approach to early in careers.
Nic Neate (35:19.81)
Yeah, and I had huge respect for those individuals. But my reaction to that was I don't really buy it. So I'm going to give you my interpretation of it, which is we need to invest in early and career developers. They will be at a loss initially because they're not as efficient as senior engineers using AI to
Ben Pearce (35:28.356)
Do just want to explain what was that kind of...
Nic Neate (35:49.326)
to do the job, but that's an important part of the process because otherwise we're going to run out of senior engineers and when the seniors all move on, we need someone to replace them. And that makes sense obviously at a level. The part I struggle with is number one, I see Microsoft and all the other big tech companies
laying off thousands and thousands and thousands of jobs, rather than investing in new careers for early and career developers. I've not seen that happening in reality. So I struggle to believe that it's really the strategy. And number two, that it doesn't acknowledge the skill set shift in its full extent in the way that it should do. So the role of a senior engineer is changing and the
The people who are best able to drive the AI function and the agents and things at the moment are the senior engineers, but they're not doing the same job that they did six months ago, a year ago. And they're evolving into a whole new job. And there's a whole new skill set as part of that that we've talked about. The thing that we need to train people for is the new skill set and the new role and not take them on the same journey.
that the current senior engineers have been on. Because you don't need to train someone on how to groom a horse in order to have a brilliant lorry driver mechanic who can function in a modern logistics company.
Ben Pearce (37:30.222)
Yeah. So, you know, time is rapidly escaping us. So if people are yet to go on this AI journey, what should people do? guess what sort of concrete actions? What would be your advice to people on what to do to make sure that they are relevant and not obsolete in this AI native team?
Nic Neate (37:54.048)
I think my number one most important thing, I'll put this first, because don't know how much time we've got, but you need to put investment in your tool chain right at the top of your priority list as an engineering function. And that's really hard to do because you are under all kinds of pressures competitively. You've got a product roadmap, which is way behind where it should be. You've got all these things that you must deliver. And getting that investment in
in internal tool chain or any kind of tech debt is always a massive challenge for an engineering team, but it's never been more important. At Microsoft, we had a team called Team Tachyon, whose purpose was to make all the other teams more efficient and go faster. They were our internal tool chain team. And we called them Team Tachyon for two reasons. One is that a Tachyon is an elementary particle which travels faster than light. And so it was all about velocity and making everyone go.
as fast as possible. And the other reason was that tachyons don't really exist. They're a hypothetical particle because nothing can travel faster than light. And it was really hard to make that team exist. We donated developers to it from other teams and they kept on getting plundered and moved on to other projects which were more obviously hitting the bottom line and delivery. Even in an organization that had hundreds of engineers just getting
three or four to work on that internal efficiency was a challenge. And you want to be putting maybe 20 % of your engineering effort into adopting an AI native approach to your function right now, because that is what it takes in order to move an existing process and team and skill set into the AI native world, getting the right NCP servers in place, getting the right agentic coding framework in place.
getting people understanding how to use that and enthusiastic and doing it as their natural way to approach their work. That's the thing that everyone's got to do.
Ben Pearce (40:02.074)
So change your tool chain to embrace AI native. That's the thing that you need to do and need to do firstly with a considerable amount of effort. And then there's all that bit that we talked about. People need a completely new set of skills that are a little bit different to what, well not a little bit, a lot different to what they've got today. And then I guess that means the people that you're hiring also need to be different and how you recruit needs to be different.
Nic Neate (40:27.168)
Exactly, you're ahead of me. If you haven't already, then your recruitment process, the tests and analysis that you put people through, that's got to completely change so that you're optimizing for the skill set that you now need, is vibe coding, is understanding how to use agents, how to do context engineering, how to create output in that new world, if you're still using leak code and coding challenges in your recruitment process.
then you're not getting the right people on board because they're going to be massively outpaced by anyone who's putting AI first in the way they do their job.
Ben Pearce (41:07.428)
Yeah. Nick, we're out of time pretty much. So let's wrap this conversation up. So what would be your key takeaways from this episode?
Nic Neate (41:21.454)
I would say that if you're on the fence about how much you should really put into AI versus just delivering what your company is very good at doing already, then you need to look very hard at that and you need to get yourself into the camp where you're changing your process, changing your skill set, changing your tool chain and planning for six months, 12 months time.
where you will not be keeping up if you aren't doing these things already. That's number one. And number two, I would say that you need to look at how do we maximize the value for the people that are working here? How do we give them the most fulfilling roles while also bringing the AI? And that's all about the training.
and understanding the motivation and having the right people in place. And it's fundamentally the most important thing for engineering leadership.
Ben Pearce (42:30.192)
Yeah and it's yeah I guess for me what's interesting is just to hear your story because your industry, your profession has been so transformed.
by AI and so many people, it is around the edges. You talk to a lot of people and you go, what are you using AI for? well, we got meeting transcripts and actions generated, but it's not fundamentally changed their core business.
And so to see that it's fundamentally changing your core business and so to hear about those war stories and the lessons that you're learning and how much you're investing into it and then the benefit you're getting from is really quite interesting.
Nic Neate (43:15.702)
Absolutely. And it's how do you take that into the other business functions as well? Because it's not just going to be software that is transformed in this way. It's everything. That's the next step.
Ben Pearce (43:20.314)
Yeah.
Ben Pearce (43:25.326)
Yeah, that's brilliant. Well, thank you so much. If people have really enjoyed what you've been saying and want to get in touch with you, how can people get in touch with you,
Nic Neate (43:35.456)
So I'm terrible with emails, phone calls, that sort of thing. So apologies if you're trying to get in touch with me and struggling. I would say that the most effective way is probably if you get a Nimbus salesperson on a call and give them the impression that you want to spend a lot of money on some maybe some bespoke API work or something, then they will inevitably pull me in.
Ben Pearce (43:57.295)
Yeah.
Nic Neate (44:04.95)
I don't really want you to waste time in our sales team. LinkedIn is the place. That's the best way to find me and engage me, absolutely. And I'm going to be at Tech Show London, so please do find me there. And I'd love to link up and have a coffee. And I'd love to hear everyone else's experience. And all the people are thinking, no, you haven't tried this, or what about this? I've got to hear those stories as well. Please bring them on.
Ben Pearce (44:30.96)
Yeah, yeah, brilliant. So final thing for me to say is thank you so much, Nick, for taking the time out of your really busy schedule to come and have this conversation and share all your experience with us.
Nic Neate (44:42.966)
It's been a joy. Thank you, Ben. Always a privilege and all the best.
Ben Pearce (44:47.268)
See you soon. Bye bye.
